DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a non-final office action in response to the application filed 30 December 2025.
Applicant’s amendments to Claims 1, 10, and 19, and addition of Claims 22 and 23 have been received and are acknowledged. Claims 2, 5 and 12 were previously cancelled.
Claims 1, 3-11, and 13-23 have been examined and are pending.
Response to Arguments
Applicant's arguments filed 30 December 2025 have been fully considered but they are not persuasive.
With regard to the rejections under 35 USC 101, Applicant argues: (1) Reiterating the argument that the recited claims do not merely recite “methods of organizing human activity” and the recited “steps could not be performed by a human…” and “…A human mind could not perform optical character recognition to extract symbols of text data …” (Applicant’s response, pg. 13-16) (2) Further Applicant reiterates that the instant recited claims “…integrate the alleged abstract idea …by using a computing system that includes various components… in a “meaningful way…more than a drafting effort designed to monopolize the exception” and…”provides an unconventional improvement in the validation of dynamic income by identifying a repeating source of deposits by identifying portions of text data that repeat and correspond to one or more credits comprising positive transaction values to dynamically generate and income amount… “ and thus “ … provide an improvement to a technical field, the claims integrate a judicial exception into a practical application…’” (Applicant’s response, pg. 16-19) (3) Applicant also reiterates that the instant claims “…recite an inventive concept that is significantly more than the alleged abstract idea … and do involve more than the performance of well-understood, routine and conventional activities previously known in the industry …’ as recited. (Applicant’s response, pg. 19-21) (4) Therefore, the claims are not directed to an abstract idea. (Applicant’s response pg. 19).
Examiner respectfully disagrees. As noted in the rejection previously and below, the invention as recited falls into the category of (methods of organizing human activity) [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. The claims as recited do not improve technology. (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….) Applicant’s own arguments state that the invention is an “…improvement in the validation of the dynamic income ….” Rather the recited claim limitations at most use known technology (recited at a high level of generality) as a tool to execute an abstract idea (See MPEP 2106.05 (f)) or merely add insignificant extra-solution activity to the judicial exception (See MPEP 2106.05 (g)). As such, the instant recited claims are at most an improvement to the abstract idea. (Applicant’s arguments 1-4).
With regard to the rejection under 35 USC 103, Examiner withdraws the prior art based on Applicant’s amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6-11, 13-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
(1) In the instant case, the claims are directed towards a method and the systems of income validation. In the instant case, Claims 19-20 are directed to a process. Claims 1, 3-4, 6-9, 21, 23 and 10-11, 13-18, 22 are directed to a system.
(2a) Prong 1: Income validation is categorized in/akin to the abstract idea subject matter grouping of: (methods of organizing human activity) [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include:
1. (Currently Amended) A dynamic income validation … comprising:
…;
a … the dynamic income validation… to:
…, …, an estimated income amount associated with a customer;
…a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determine, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
determining whether the repeating source of deposits is associated with known merchants or known payment sources;
determining whether the income amount is within a predetermined distance from the estimated income amount;
responsive to determining (i) the income amount is within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is associated with the known merchants or the known payment sources, assigning a first score as the confidence score; and
responsive to determining (i) the income amount is not within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is not associated with the known merchants or the known payment sources, assigning a second score as the confidence score[[;]],
wherein the second …comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generate a modified … comprising the income amount and the confidence score;
dynamically … the modified … to a second user device for display; and
dynamically … the first and second … based on one or more of the repeating source of deposits, the income amount, the confidence score, or combinations thereof.
10. (Currently Amended) A dynamic income validation …comprising:
…;
… the dynamic income validation … to:
… an estimated income amount associated with a customer;
… a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determine, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
determining whether the repeating source of deposits is associated with known merchants or known payment sources;
determining whether the income amount is within a predetermined distance from the estimated income amount;
responsive to determining (i) the income amount is within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is associated with the known merchants or the known payment sources, assigning a first score as the confidence score; and
responsive to determining (i) the income amount is not within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is not associated with the known merchants or the known payment sources, assigning a second score as the confidence score[[;]],
wherein the second …comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generate a modified … comprising the income amount and the confidence score;
dynamically … the modified … to a second user device for display; and
dynamically … the first and second … based on one or more of the repeating source of deposits, the income amount, the confidence score, or combinations thereof.
19. (Currently Amended) A … implemented method comprising:
…, …, an estimated income amount associated with a customer;
… a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determining, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generating, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
determining whether the repeating source of deposits is associated with known merchants or known payment sources;
determining whether the income amount is within a predetermined distance from the estimated income amount;
responsive to determining (i) the income amount is within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is associated with the known merchants or the known payment sources, assigning a first score as the confidence score; and
responsive to determining (i) the income amount is not within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is not associated with the known merchants or the known payment sources, assigning a second score as the confidence score,
wherein the second…comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generating a modified … comprising the income amount and the estimated income amount;
dynamically … the modified … to a second user device for display; and
dynamically … the first and second … based on one or more of the repeating source of deposits, the income amount, or combinations thereof.
As stated above, this abstract idea falls into the (b) subject matter grouping of: (methods of organizing human activity) .
Prong 2: When considered individually and in combination, the instant claims are do not integrate the exception into a practical application because the steps of or retrieve… determine by … extracting… and identifying…; … generate by… determining… determining… assigning… assigning…;…parses…; …determines;…identifies; …flags..; removes….; classifies …; ….excludes… generate.. . do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. storing…receive… receive or retrieve…extracting... transmitting…for display…updating…) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely recite: “apply it” (or an equivalent), merely include instructions to implement an abstract idea on a computer or merely uses generic computing elements to perform well known, routine, and conventional functions or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (d), (f) and (g))
(2b) In the instant case, Claims 19-20 are directed to a process. Claims 1, 3-4, 6-9, 21, 23 and 10-11, 13-18, 22 are directed to a system.
Additionally, the claims (independent and dependent) do not include additional elements that individually or in combination are sufficient to amount to significantly more than the judicial exception of abstract idea (i.e. provide an inventive concept). As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: system, processors, memory, device, machine learning model(s) , graphical user interface merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception (See MPEP 2106.05 (f) and (g)) (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….)
The dependent claims have also been examined and do not correct the deficiencies of the independent claims.
It is noted that claim (3-4, 6-9, 11, 13-18 and 20-23) introduce the additional elements of wherein clauses further defining claim elements (Claims 3 and 13, 4 and 15, 5, 6, 21…); determine… responsible to determining…generate… transmit …(Claim 7); determine... responsive to determining… modify….(Claims 8, 9 and 18); receive… (Claim 11); generate…determine… responsive to determining… generate…transmit… (Claim 14); generate…responsive to generating…(Claim 17 and 20)…analyzing… identifying… determining… identifying… (Claim 21)..These elements are not a practical application of the judicial exception (i.e. the abstract idea) because the limitations merely recite: “apply it” (or an equivalent), merely include instructions to implement an abstract idea on a computer or merely uses generic computing elements to perform well known, routine, and conventional functions or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (f) and (g))
Further these limitations ( system, processors, memory, device, machine learning model(s) , graphical user interface )taken alone or in combination with the abstract do not amount to significantly more than the abstract idea alone because the elements amount to mere use of a computer a as tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions.(See MPEP 2106.05 (f) and (g)) (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….)
Therefore, claims 1, 3-4, 6-11, and 13-23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Prior Art
The closest prior art of record: US 2019/0043127 Al, Mahapatra et al. hereinafter referred to Mahapatra discloses a method and system of verifying income using neural networks and search queries. US 10,796,380 Bl, Mossoba et al. hereinafter referred to as Mossoba is merely another method and system employment status detection including income verification features using a transaction log (i.e. a plurality of transactions). US 2023/0008975 A1, Crudele et al. hereinafter referred to as Crudele is another method and system for verifying an identity of a user based on a data mesh in which the data includes deposits from an employer. Even though the prior art of record discloses the general concepts cited above, the prior art of record fails to teach a second machine learning model which differentiates types of deposits, flags credits for review, classifies the transactions and excludes non-repeating gifts from the income calculations. The specific claim language that the prior art of record fails to teach is the combination of:
dynamically determine, using a first machine learning model, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second machine learning model, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
determining whether the repeating source of deposits is associated with known merchants or known payment sources;
determining whether the income amount is within a predetermined distance from the estimated income amount;
responsive to determining (i) the income amount is within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is associated with the known merchants or the known payment sources, assigning a first score as the confidence score; and
responsive to determining (i) the income amount is not within the predetermined distance from the estimated income amount, and (ii) the repeating source of deposits is not associated with the known merchants or the known payment sources, assigning a second score as the confidence score,
wherein the second machine learning model comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a graphical user interface; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ASHA PUTTAIA H/Primary Examiner, Art Unit 3691
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